def __init__(self):
device = torch.device('cpu')
FORCE_TO_CPU = True
parser = argument_parser()
args = parser.parse_args(['PETA', '--model=dpn107'])
visenv_name = 'PETA'
exp_dir = os.path.join('exp_result', visenv_name)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(
model_dir, 'ckpt_max_e0384293_2020-09-17_18-35-21.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
_, predict_tsfm = get_transform(args)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=predict_tsfm)
args.att_list = valid_set.attr_id
backbone = getattr(sys.modules[__name__], args.model)()
if "dpn68" in args.model:
net_parameter = 832
elif "dpn" in args.model:
net_parameter = 2688
elif "densenet" in args.model:
net_parameter = 1024
else:
net_parameter = 2048
classifier = BaseClassifier(netpara=net_parameter,
nattr=valid_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available() and not FORCE_TO_CPU:
model = torch.nn.DataParallel(model).cuda()
ckpt = torch.load(save_model_path)
print(f'Model is served with GPU ')
else:
model = torch.nn.DataParallel(model)
ckpt = torch.load(save_model_path,
map_location=torch.device('cpu'))
print(f'Model is served with CPU ')
model.load_state_dict(ckpt['state_dicts'])
model.eval()
# from torchsummary import summary
# summary(model, input_size=(3, 256, 192))
print('Total number of parameters: ',
sum(p.numel() for p in model.parameters() if p.requires_grad))
self.args = args
self.predict_tsfm = predict_tsfm
self.model = model
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
pin_memory=True,
)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=4,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
backbone = resnet50()
classifier = BaseClassifier(nattr=35)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
print("reloading pretrained models")
exp_dir = os.path.join('exp_result', args.dataset)
model_path = os.path.join(exp_dir, args.dataset, 'img_model')
model.load_state_dict(
torch.load(
'/home/sohaibrabbani/PycharmProjects/Strong_Baseline_of_Pedestrian_Attribute_Recognition/pedestrian_model/rap2_ckpt_max.pth'
)['state_dicts'])
# model = get_reload_weight(model_path, model)
model.eval()
preds_probs = []
gt_list = []
with torch.no_grad():
for step, (imgs, gt_label, imgname) in enumerate(tqdm(valid_loader)):
imgs = imgs.cuda()
gt_label = gt_label.cuda()
gt_list.append(gt_label.cpu().numpy())
gt_label[gt_label == -1] = 0
valid_logits = model(imgs)
valid_probs = torch.sigmoid(valid_logits)
preds_probs.append(valid_probs.cpu().numpy())
gt_label = np.concatenate(gt_list, axis=0)
preds_probs = np.concatenate(preds_probs, axis=0)
valid_result = get_pedestrian_metrics(gt_label, preds_probs)
print(
f'Evaluation on test set, \n',
'ma: {:.4f}, pos_recall: {:.4f} , neg_recall: {:.4f} \n'.format(
valid_result.ma, np.mean(valid_result.label_pos_recall),
np.mean(valid_result.label_neg_recall)),
'Acc: {:.4f}, Prec: {:.4f}, Rec: {:.4f}, F1: {:.4f}'.format(
valid_result.instance_acc, valid_result.instance_prec,
valid_result.instance_recall, valid_result.instance_f1))
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join(args.save_path, args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
os.environ['CUDA_VISIBLE_DEVICES'] = args.device
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
#train_tsfm, valid_tsfm = get_transform(args)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
#train_set = AttrDataset(args=args, split=args.train_split, transform=train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
pin_memory=True,
)
#valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=4,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
sample_weight = labels.mean(0)
#backbone = resnet50()
if args.model_name == 'resnet50':
backbone = resnet50()
if args.model_name == 'resnet18':
backbone = resnet18()
if args.model_name == 'resnet50_dynamic_se':
backbone = resnet50_dynamic_se()
if args.model_name == 'resnet18_dynamic_se':
backbone = resnet18_dynamic_se()
if args.model_name == 'resnet18_replace_se':
backbone = resnet18_replace_se()
if args.model_name == 'resnet18_se':
backbone = resnet18_se()
if args.model_name == 'resnet34':
backbone = resnet34()
if args.model_name == 'acnet':
backbone = resnet18_acnet(num_classes=train_set.attr_num)
print('have generated the model')
classifier = BaseClassifier(nattr=train_set.attr_num)
classifier_depth = BaseClassifier(nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
print('')
#if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = CEL_Sigmoid(sample_weight)
param_groups = [{
'params': model.module.finetune_params(),
'lr': args.lr_ft
}, {
'params': model.module.fresh_params(),
'lr': args.lr_new
}]
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
loss = args.loss
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path,
loss=loss)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join(args.save_path, args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
os.environ['CUDA_VISIBLE_DEVICES'] = args.device
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}')
#train_tsfm, valid_tsfm = get_transform(args)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
#train_set = AttrDataset(args=args, split=args.train_split, transform=train_tsfm)
train_set = AttrDataset(args=args, split=args.train_split, transform=train_tsfm, target_transform=None, Type='train' )
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
drop_last=True,
pin_memory=True,
)
#valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm, target_transform=None, Type='val')
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=4,
drop_last=True,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
sample_weight = labels.mean(0)
#backbone = resnet50()
if args.model_name == 'resnet50':
backbone = resnet50()
if args.model_name == 'resnet18':
backbone = resnet18()
if args.model_name == 'resnet18_stn':
backbone = resnet18_stn()
if args.model_name == 'resnet_depth':
backbone = resnet_depth()
if args.model_name == 'resnet18_transformer':
backbone = resnet18_transformer()
if args.model_name == 'resnet50_dynamic_se':
backbone = resnet50_dynamic_se()
if args.model_name == 'resnet18_dynamic_se':
backbone = resnet18_dynamic_se()
if args.model_name == 'resnet18_replace_se':
backbone = resnet18_replace_se()
if args.model_name == 'resnet18_se':
backbone = resnet18_se()
if args.model_name == 'resnet34':
backbone = resnet34()
if args.model_name == 'resnet18_group_se':
backbone = resnet18_group_se()
if args.model_name == 'resnet18_vit':
backbone = resnet18_vit()
if args.model_name == 'resnet18_vit_v2':
backbone = resnet18_vit_v2()
if args.model_name == 'resnet18_vit_v3':
backbone = resnet18_vit_v3()
if args.model_name == 'resnet18_vit_v5':
backbone = resnet18_vit_v5()
if args.model_name == 'resnet18_energy_vit':
backbone = resnet18_energy_vit()
if args.model_name == 'resnet18_vit_split':
backbone = resnet18_vit_split(num_classes = train_set.attr_num)
if args.model_name == 'inception_self':
backbone = inception_self()
if args.model_name == 'spatial_modulator':
backbone = spatial_modulator()
if args.model_name == 'fusion_concat':
backbone = fusion_concat()
print('have generated the model')
classifier = BaseClassifier(nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
print('')
#if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
#for k, v in model.state_dict().items():
# print(k)
'''
model_dict = {}
state_dict = model.state_dict()
pretrain_dict = torch.load('/home/pengqy/paper/resnet18_2/PETA/PETA/img_model/ckpt_max.pth')['state_dicts']
for k, v in pretrain_dict.items():
# print('%%%%% ', k)
if k in state_dict:
if k.startswith('module.backbone.conv1'):
#pdb.set_trace()
model_dict[k] = v
elif k.startswith('module.backbone.bn1'):
model_dict[k] = v
elif k.startswith('module.backbone.layer'):
model_dict[k] = v
elif k.startswith('module.classifier'):
model_dict[k] = v
#elif k.startswith('module.backbone.spa_conv_0'):
# model_dict[k] = v
#elif k.startswith('module.backbone.spa_bn_0'):
# model_dict[k] = v
#elif k.startswith('module.classifier'):
# model_dict[k] = v
#elif k.startswith('module.classifier'):
# model_dict[k] = v
#pdb.set_trace()
state_dict.update(model_dict)
model.load_state_dict(state_dict)
'''
criterion = CEL_Sigmoid(sample_weight)
param_groups = [{'params': model.module.finetune_params(), 'lr':0.01},
{'params': model.module.fresh_params(), 'lr':0.1}]
optimizer = torch.optim.SGD(param_groups, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
loss = args.loss
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path,
loss =loss)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
def main(cfg, args):
exp_dir = os.path.join('exp_result', cfg.DATASET.NAME)
# model_name = cfg.NAME
# if not cfg.use_bn:
# model_name += '_wobn'
model_dir, log_dir = get_model_log_path(exp_dir, cfg.NAME)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, f'ckpt_max_{time_str()}.pth')
visdom = None
if cfg.VIS.VISDOM:
visdom = Visdom(env=f'{cfg.DATASET.NAME}_' + cfg.NAME, port=8401)
assert visdom.check_connection()
writer = None
if cfg.VIS.TENSORBOARD.ENABLE:
current_time = datetime.now().strftime('%b%d_%H-%M-%S')
writer_dir = os.path.join(exp_dir, cfg.NAME, 'runs', current_time)
writer = SummaryWriter(log_dir=writer_dir)
if cfg.REDIRECTOR:
if args.local_rank == 0:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
if 'WORLD_SIZE' in os.environ:
args.distributed = int(os.environ['WORLD_SIZE']) > 1
else:
args.distributed = None
args.world_size = 1
args.rank = 0 # global rank
if args.distributed:
args.device = 'cuda:%d' % args.local_rank
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl',
init_method='env://')
args.world_size = torch.distributed.get_world_size()
args.rank = torch.distributed.get_rank()
# print(f'use GPU{args.device} for training')
# print(args.world_size, args.rank)
print(
'Training in distributed mode with multiple processes, 1 GPU per process. Process %d, total %d.'
% (args.rank, args.world_size))
else:
print('Training with a single process on 1 GPUs.')
# pprint.pprint(OrderedDict(cfg.__dict__))
if args.local_rank == 0:
print(cfg)
train_tsfm, valid_tsfm = get_transform(cfg)
if args.local_rank == 0:
print(train_tsfm)
if cfg.DATASET.TYPE == 'multi_label':
train_set = COCO14(cfg=cfg,
split=cfg.DATASET.TRAIN_SPLIT,
transform=train_tsfm,
target_transform=cfg.DATASET.TARGETTRANSFORM)
valid_set = COCO14(cfg=cfg,
split=cfg.DATASET.VAL_SPLIT,
transform=valid_tsfm,
target_transform=cfg.DATASET.TARGETTRANSFORM)
else:
assert False, ''
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_set)
valid_sampler = torch.utils.data.distributed.DistributedSampler(
valid_set, shuffle=False)
else:
train_sampler = None
valid_sampler = None
train_loader = DataLoader(
dataset=train_set,
batch_size=cfg.TRAIN.BATCH_SIZE //
dist.get_world_size() if args.distributed else 16,
sampler=train_sampler,
shuffle=train_sampler is None,
num_workers=4,
pin_memory=True,
)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=cfg.TRAIN.BATCH_SIZE //
dist.get_world_size() if args.distributed else 16,
sampler=valid_sampler,
shuffle=False,
num_workers=4,
pin_memory=False,
)
if args.local_rank == 0:
print('-' * 60)
print(
f'{cfg.DATASET.NAME} attr_num : {train_set.attr_num}, eval_attr_num : {train_set.eval_attr_num} '
f'{cfg.DATASET.TRAIN_SPLIT} set: {len(train_loader.dataset)}, '
f'{cfg.DATASET.TEST_SPLIT} set: {len(valid_loader.dataset)}, ')
labels = train_set.label
label_ratio = labels.mean(0) if cfg.LOSS.SAMPLE_WEIGHT else None
backbone = model_dict[cfg.BACKBONE.TYPE][0]
# backbone = TResnetL()
# state = torch.load('./pretrained/tresnet_l.pth', map_location='cpu')
# filtered_dict = {k: v for k, v in state['model'].items() if
# (k in backbone.state_dict() and 'head.fc' not in k)}
# backbone.load_state_dict(filtered_dict, strict=False)
classifier = classifier_dict[cfg.CLASSIFIER.TYPE](
nattr=train_set.attr_num,
c_in=model_dict[cfg.BACKBONE.TYPE][1],
# c_in=2432,
bn=cfg.CLASSIFIER.BN,
pool=cfg.CLASSIFIER.POOLING,
scale=cfg.CLASSIFIER.SCALE,
)
model = FeatClassifier(backbone, classifier, bn_wd=cfg.TRAIN.BN_WD)
if args.local_rank == 0:
print(
f"backbone: {cfg.BACKBONE.TYPE}, classifier: {cfg.CLASSIFIER.TYPE}"
)
print(f"model_name: {cfg.NAME}")
# with torch.cuda.device(0):
# model = model.cuda()
#
# macs, params = get_model_complexity_info(model, (3, 224, 224), as_strings=False,
# print_per_layer_stat=False)
# print('{:<30} {:<8}'.format('Computational complexity: ', macs))
# print('{:<30} {:<8}'.format('Number of parameters: ', params))
model = model.cuda()
if args.distributed:
# model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.local_rank], broadcast_buffers=False)
else:
model = torch.nn.DataParallel(model)
model_ema = None
if cfg.TRAIN.EMA.ENABLE:
# Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper
model_ema = ModelEmaV2(
model,
decay=cfg.TRAIN.EMA.DECAY,
device='cpu' if cfg.TRAIN.EMA.FORCE_CPU else None)
if cfg.RELOAD.TYPE:
reload_path = os.path.join(exp_dir, cfg.RELOAD.NAME, 'img_model')
model = get_reload_weight(reload_path, model)
loss_weight = cfg.LOSS.LOSS_WEIGHT
criterion = loss_dict[cfg.LOSS.TYPE](sample_weight=label_ratio)
if cfg.TRAIN.BN_WD:
param_groups = [{
'params': model.module.finetune_params(),
'lr': cfg.TRAIN.LR_SCHEDULER.LR_FT,
'weight_decay': cfg.TRAIN.OPTIMIZER.WEIGHT_DECAY
}, {
'params': model.module.fresh_params(),
'lr': cfg.TRAIN.LR_SCHEDULER.LR_NEW,
'weight_decay': cfg.TRAIN.OPTIMIZER.WEIGHT_DECAY
}]
else:
# bn parameters are not applied with weight decay
ft_params = seperate_weight_decay(
model.module.finetune_params(),
lr=cfg.TRAIN.LR_SCHEDULER.LR_FT,
weight_decay=cfg.TRAIN.OPTIMIZER.WEIGHT_DECAY)
fresh_params = seperate_weight_decay(
model.module.fresh_params(),
lr=cfg.TRAIN.LR_SCHEDULER.LR_NEW,
weight_decay=cfg.TRAIN.OPTIMIZER.WEIGHT_DECAY)
param_groups = ft_params + fresh_params
if cfg.TRAIN.OPTIMIZER.TYPE.lower() == 'sgd':
optimizer = torch.optim.SGD(param_groups,
momentum=cfg.TRAIN.OPTIMIZER.MOMENTUM)
elif cfg.TRAIN.OPTIMIZER.TYPE.lower() == 'adam':
optimizer = torch.optim.Adam(param_groups)
elif cfg.TRAIN.OPTIMIZER.TYPE.lower() == 'adamw':
optimizer = AdamW(param_groups)
else:
assert None, f'{cfg.TRAIN.OPTIMIZER.TYPE} is not implemented'
if cfg.TRAIN.LR_SCHEDULER.TYPE == 'plateau':
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
if cfg.CLASSIFIER.BN:
assert False, 'BN can not compatible with ReduceLROnPlateau'
elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'multistep':
lr_scheduler = MultiStepLR(optimizer,
milestones=cfg.TRAIN.LR_SCHEDULER.LR_STEP,
gamma=0.1)
elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'warmup_cosine':
lr_scheduler = CosineLRScheduler(
optimizer,
t_initial=cfg.TRAIN.MAX_EPOCH,
lr_min=1e-5,
warmup_lr_init=1e-4,
warmup_t=cfg.TRAIN.MAX_EPOCH * cfg.TRAIN.LR_SCHEDULER.WMUP_COEF,
)
elif cfg.TRAIN.LR_SCHEDULER.TYPE == 'onecycle':
lr_scheduler = OneCycleLR(optimizer,
max_lr=cfg.TRAIN.LR_SCHEDULER.LR_NEW,
steps_per_epoch=len(train_loader),
epochs=40,
pct_start=0.2)
if args.local_rank == 0:
print(f'steps_per_epoch {len(train_loader)}')
else:
assert False, f'{cfg.LR_SCHEDULER.TYPE} has not been achieved yet'
best_metric, epoch = trainer(cfg,
args,
epoch=cfg.TRAIN.MAX_EPOCH,
model=model,
model_ema=model_ema,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path,
loss_w=loss_weight,
viz=visdom,
tb_writer=writer)
if args.local_rank == 0:
print(f'{cfg.NAME}, best_metrc : {best_metric} in epoch{epoch}')
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
log_name = time.strftime("%Y-%m-%d %H:%M:%S", time.localtime(time.time()))
stdout_file = os.path.join(log_dir, f'stdout_{log_name}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
pin_memory=True,
)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=16,
shuffle=False,
num_workers=4,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
sample_weight = labels.mean(0)
# backbone = resnet50()
# classifier = BaseClassifier(nattr=train_set.attr_num)
backbone = osnet_ain_x1_0(num_classes=56, pretrained=True, loss='softmax')
classifier = BaseClassifier_osnet(nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
#改
criterion = CEL_Sigmoid(sample_weight)
# criterion = multilabel_categorical_crossentropy
param_groups = [{
'params': model.module.finetune_params(),
'lr': args.lr_ft
}, {
'params': model.module.fresh_params(),
'lr': args.lr_new
}]
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
if args.training: #训练，每隔1个epoch，验证集评估一次
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path,
dataset=train_set)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
else: #仅评估，不训练
model_path = args.best_model_path
saved_state_dict = torch.load(model_path)['state_dicts']
saved_state_dict = {
k.replace('module.', ''): v
for k, v in saved_state_dict.items()
}
backbone = osnet_ain_x1_0(num_classes=valid_set.attr_num,
pretrained=True,
loss='softmax')
print('make model for only test')
classifier = BaseClassifier_osnet(nattr=valid_set.attr_num)
test_model = FeatClassifier(backbone, classifier)
print("loading model")
test_model.load_state_dict(saved_state_dict)
test_model.cuda()
test_alm(valid_loader,
test_model,
attr_num=valid_set.attr_num,
description=valid_set.attr_id,
set='test',
threshold=0.5)
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}')
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
train_set = AttrDataset(args=args, split=args.train_split, transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=0,
pin_memory=True,
)
valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=0,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
# sample_weight = labels.mean(0)
# sample_weight = labels[labels!=2].reshape((labels.shape[0], labels.shape[1])).mean(0)
sample_weight = np.nanmean(np.where(labels!=2,labels,np.nan), axis=0)
backbone = getattr(sys.modules[__name__], args.model)()
if "dpn68" in args.model:
net_parameter = 832
elif "dpn" in args.model:
net_parameter = 2688
elif "densenet" in args.model:
net_parameter = 1024
else:
net_parameter = 2048
classifier = BaseClassifier(netpara=net_parameter, nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = CEL_Sigmoid(sample_weight)
if torch.cuda.is_available():
param_groups = [{'params': model.module.finetune_params(), 'lr': args.lr_ft},
{'params': model.module.fresh_params(), 'lr': args.lr_new}]
else:
param_groups = [{'params': model.finetune_params(), 'lr': args.lr_ft},
{'params': model.fresh_params(), 'lr': args.lr_new}]
optimizer = torch.optim.SGD(param_groups, momentum=args.momentum, weight_decay=args.weight_decay, nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join(args.save_path, args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
os.environ['CUDA_VISIBLE_DEVICES'] = args.device
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
#train_tsfm, valid_tsfm = get_transform(args)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
#train_set = AttrDataset(args=args, split=args.train_split, transform=train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
pin_memory=True,
)
#valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=4,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
sample_weight = labels.mean(0)
#backbone = resnet50()
if args.model_name == 'resnet18':
backbone = resnet18()
if args.model_name == 'acnet':
backbone = resnet18_acnet(num_classes=train_set.attr_num)
if args.model_name == 'resnet18_attention_depth':
backbone = resnet18_attention_depth(num_classes=train_set.attr_num)
if args.model_name == 'resnet18_no_attention_depth':
backbone = resnet18_no_attention_depth(num_classes=train_set.attr_num)
if args.model_name == 'resnet_depth_selective_fusion':
backbone = resnet_depth_selective_fusion(
num_classes=train_set.attr_num)
if args.model_name == 'resnet_attention_depth_spatial':
backbone = resnet_attention_depth_spatial(
num_classes=train_set.attr_num)
if args.model_name == 'resnet_attention_depth_cbam_spatial':
backbone = resnet_attention_depth_cbam_spatial(
num_classes=train_set.attr_num)
if args.model_name == 'resnet18_inception_depth_4':
backbone = resnet18_inception_depth_4(num_classes=train_set.attr_num)
if args.model_name == 'resnet18_self_attention_depth_34':
backbone = resnet18_self_attention_depth_34(
num_classes=train_set.attr_num)
if args.model_name == 'resnet18_resnet18_resnet18_34':
backbone = resnet18_resnet18_resnet18_34(
num_classes=train_set.attr_num)
if args.model_name == 'resnet18_self_attention_depth_34_version2':
backbone = resnet18_self_attention_depth_34_version2(
num_classes=train_set.attr_num)
if args.model_name == 'resnet18_inception_depth_4_wrap':
backbone = resnet18_inception_depth_4_wrap(
num_classes=train_set.attr_num)
if args.model_name == 'ours':
backbone = ours(num_classes=train_set.attr_num)
if args.model_name == 'resnet50_ours':
backbone = resnet50_ours(num_classes=train_set.attr_num)
if args.model_name == 'resnet_attention':
backbone = resnet_attention(num_classes=train_set.attr_num)
if args.model_name == 'resnet18_self_mutual_attention':
backbone = resnet18_self_mutual_attention(
num_classes=train_set.attr_num)
print('have generated the model')
classifier = BaseClassifier(nattr=train_set.attr_num)
#classifier_depth = BaseClassifier(nattr=train_set.attr_num)
#model = FeatClassifier(backbone, classifier, classifier_depth)
model = FeatClassifier(backbone, classifier)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
print('')
#if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
#pdb.set_trace()
model_dict = {}
state_dict = model.state_dict()
pretrain_dict = torch.load(
'/media/data1/pengqy/Strong_Baseline_of_Pedestrian_Attribute_Recognition/resnet18_rgb/PETA/PETA/img_model/ckpt_max.pth'
)['state_dicts']
for k, v in pretrain_dict.items():
# print('%%%%% ', k)
if k in state_dict:
if k.startswith('module.backbone.conv1'):
model_dict[k] = v
elif k.startswith('module.backbone.bn1'):
model_dict[k] = v
elif k.startswith('module.backbone.layer'):
model_dict[k] = v
elif k.startswith('module.classifier'):
model_dict[k] = v
pdb.set_trace()
pretrain_dict = torch.load(
'/media/data1/pengqy/Strong_Baseline_of_Pedestrian_Attribute_Recognition/resnet_depth/PETA/PETA/img_model/ckpt_max.pth'
)['state_dicts']
for k, v in pretrain_dict.items():
# print('%%%%% ', k)
if k in state_dict:
if k.startswith('module.backbone.conv1'):
model_dict[k] = v
elif k.startswith('module.backbone.bn1'):
model_dict[k] = v
elif k.startswith('module.backbone.layer'):
model_dict[k] = v
pdb.set_trace()
state_dict.update(model_dict)
criterion = CEL_Sigmoid(sample_weight)
'''
param_groups = [{'params': model.module.finetune_params(), 'lr': args.lr_ft},
{'params': model.module.fresh_params(), 'lr': args.lr_new},
{'params': model.module.fresh_depth_params(), 'lr': args.lr_new}]
'''
param_groups = [{
'params': model.module.finetune_params(),
'lr': 0.01
}, {
'params': model.module.fresh_params(),
'lr': 0.01
}]
'''
param_groups = [{'params': model.module.finetune_params(), 'lr': args.lr_ft},
{'params': model.module.fresh_params(), 'lr': args.lr_new},
{'params': model.module.depth_params(), 'lr': 0.005}]
'''
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
loss = args.loss
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path,
loss=loss)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID'
os.environ['CUDA_VISIBLE_DEVICES'] = str(args.device)
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
pin_memory=True,
)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=4,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
sample_weight = labels.mean(0)
backbone = resnet50()
classifier = BaseClassifier(nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = CEL_Sigmoid(sample_weight)
param_groups = [{
'params': model.module.finetune_params(),
'lr': args.lr_ft
}, {
'params': model.module.fresh_params(),
'lr': args.lr_new
}]
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = MultiStepLR(optimizer, args.lr_scheduler_steps, gamma=0.1)
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
torch.save(model.state_dict(),
os.path.join(model_dir, f'{time_str()}_model.pth.tar'))
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
pin_memory=True,
)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=4,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
sample_weight = labels.mean(0)
if args.model == 'resnet18':
backbone = resnet18()
elif args.model == 'resnet34':
backbone = resnet34()
elif args.model == 'resnet50':
backbone = resnet50()
elif args.model == 'resnet101':
backbone = resnet101()
elif args.model == 'resnet152':
backbone = resnet50()
else:
raise ValueError('No Defined Model!')
classifier = BaseClassifier(nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = CEL_Sigmoid(sample_weight)
param_groups = [{
'params': model.module.finetune_params(),
'lr': args.lr_ft
}, {
'params': model.module.fresh_params(),
'lr': args.lr_new
}]
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
# Added for logging purposes
user = getpass.getuser()
fixed_time_str = time_str()
stdout_file = os.path.join(
log_dir, "_".join(['stdout', user, f'{fixed_time_str}.txt']))
save_model_path = os.path.join(
model_dir, "_".join(['ckpt_max', user, f'{fixed_time_str}.pth']))
trackitems_dir = os.path.join(
log_dir, "_".join(['trackitems', user, f'{fixed_time_str}.txt']))
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
#train_set = AttrDataset(args=args, split=args.train_split, transform=train_tsfm)
train_set = AttrDataset_new(args=args,
split=args.train_split,
transformation_dict=args.train_transform)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=8,
pin_memory=True,
)
#valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_set = AttrDataset_new(args=args,
split=args.valid_split,
transformation_dict=args.valid_transform)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=8,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
# sample_weight = labels.mean(0)
sample_weight = np.nanmean(np.where(labels != 2, labels, np.nan), axis=0)
backbone = getattr(sys.modules[__name__], args.model)()
if "dpn68" in args.model:
net_parameter = 832
elif "dpn" in args.model:
net_parameter = 2688
elif "densenet" in args.model:
net_parameter = 1024
else:
net_parameter = 2048
classifier = BaseClassifier(netpara=net_parameter,
nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = CEL_Sigmoid(sample_weight)
if torch.cuda.is_available():
param_groups = [{
'params': model.module.finetune_params(),
'lr': args.lr_ft
}, {
'params': model.module.fresh_params(),
'lr': args.lr_new
}]
else:
param_groups = [{
'params': model.finetune_params(),
'lr': args.lr_ft
}, {
'params': model.fresh_params(),
'lr': args.lr_new
}]
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
# Added for logging purposes
with open(trackitems_dir, "a") as f:
code, line_no = inspect.getsourcelines(get_transform)
for line in code:
f.write(str(line))
f.write(str("\n\n"))
f.write(str(args.__dict__))
f.write(str("\n\n"))
f.write(str(lr_scheduler.__dict__))
f.write(str("\n\n"))
model_str = str(model).lower()
have_dropout = 'dropout' in model_str
f.write('dropout: %s' % (have_dropout))
f.write(str("\n\n"))
have_leaky_relu = 'leaky_relu' in model_str
f.write('leaky_relu: %s' % (have_leaky_relu))
f.write(str("\n\n"))
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path,
measure="f1")
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
# Added for logging purposes
with open(trackitems_dir, "a") as f:
f.write(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
f.write("\n\n")
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join(args.save_path, args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
os.environ['CUDA_VISIBLE_DEVICES'] = args.device
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
#train_tsfm, valid_tsfm = get_transform(args)
train_tsfm, train_tsfm_resize, valid_tsfm, valid_tsfm_resize = get_transform(
args)
print(train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm,
transform_resize=train_tsfm_resize)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=0,
pin_memory=True,
)
#valid_set = AttrDataset(args=args, split=args.valid_split, transform=valid_tsfm)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm,
transform_resize=valid_tsfm_resize)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=0,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
sample_weight = labels.mean(0)
if args.model_name == 'resnet18_consistent':
backbone = resnet18_consistent()
print('have generated the model')
classifier = BaseClassifier(nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
print('Number of model parameters: {}'.format(
sum([p.data.nelement() for p in model.parameters()])))
print('')
#if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
#for k, v in model.state_dict().items():
# print(k)
model_dict = {}
state_dict = model.state_dict()
#pretrain_dict = model_zoo.load_url(model_urls['resnet18'])
pretrain_dict = torch.load('ckpt_max_23.pth')['state_dicts']
for k, v in pretrain_dict.items():
#print(k)
#if k in state_dict:
#if k.startswith('module.backbone.conv1'):
#if k.startswith('module.backbone.fix'):
#pdb.set_trace()
# model_dict['module.backbone'+k[19:]] = v
# #model_dict['module.backbone.fix'+k[15:]] = v
if k.startswith('module.backbone.'):
# #pdb.set_trace()
model_dict[k] = v
if k.startswith('module.classifier.conv'):
#pdb.set_trace()
model_dict['module.backbone.conv' + k[22:]] = v
elif k.startswith('module.classifier.bn'):
#pdb.set_trace()
model_dict['module.backbone.bn' + k[20:]] = v
#elif k.startswith('layer3'):
# model_dict['module.backbone.fix.'+k] = v
#elif k.startswith('layer4'):
# model_dict['module.backbone.fix.'+k] = v
#elif k.startswith('module.classifier.conv_3'):
# model_dict['module.backbone.fix'+k[17:]] = v
#elif k.startswith('module.classifier'):
# model_dict[k] = v
#elif k.startswith('module.classifier'):
# model_dict[k] = v
#pdb.set_trace()
#model_dict['module.backbone.fix.keypoints_0'] = part_init_0
#model_dict['module.backbone.fix.keypoints_1'] = part_init_1
#model_dict['module.backbone.fix.keypoints_2'] = part_init_2
#for k , v in state_dict.items():
# print(k)
#print('sss')
#for k, v in state_dict.items():
# print(k)
#pdb.set_trace()
state_dict.update(model_dict)
model.load_state_dict(state_dict)
for name, child in model.module.backbone.named_children():
#print(name)
if name == 'fix':
#pdb.set_trace()
for param in child.parameters():
#pdb.set_trace()
#print('sss')
param.requires_grad = True
#pdb.set_trace()
criterion = CEL_Sigmoid(sample_weight)
#model.load_state_dict(torch.load('/home/pengqy/paper/resnet18_consist/PETA/PETA/img_model/ckpt_max.pth')['state_dicts'])
param_groups = [
{
'params': model.module.finetune_params(),
'lr': 0.0001
},
#{'params': model.module.stn_params(), 'lr': 0.0001},
{
'params': model.module.fresh_params(),
'lr': 0.1
}
]
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
loss = args.loss
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path,
loss=loss)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
def main(args):
visenv_name = args.dataset
exp_dir = os.path.join('exp_result', args.dataset)
model_dir, log_dir = get_model_log_path(exp_dir, visenv_name)
stdout_file = os.path.join(log_dir, f'stdout_{time_str()}.txt')
save_model_path = os.path.join(model_dir, 'ckpt_max.pth')
if args.redirector:
print('redirector stdout')
ReDirectSTD(stdout_file, 'stdout', False)
pprint.pprint(OrderedDict(args.__dict__))
print('-' * 60)
print(f'use GPU{args.device} for training')
print(
f'train set: {args.dataset} {args.train_split}, test set: {args.valid_split}'
)
train_tsfm, valid_tsfm = get_transform(args)
print(train_tsfm)
train_set = AttrDataset(args=args,
split=args.train_split,
transform=train_tsfm)
train_loader = DataLoader(
dataset=train_set,
batch_size=args.batchsize,
shuffle=True,
num_workers=4,
pin_memory=True,
)
valid_set = AttrDataset(args=args,
split=args.valid_split,
transform=valid_tsfm)
valid_loader = DataLoader(
dataset=valid_set,
batch_size=args.batchsize,
shuffle=False,
num_workers=4,
pin_memory=True,
)
print(f'{args.train_split} set: {len(train_loader.dataset)}, '
f'{args.valid_split} set: {len(valid_loader.dataset)}, '
f'attr_num : {train_set.attr_num}')
labels = train_set.label
sample_weight = labels.mean(0)
backbone = resnet50()
classifier = BaseClassifier(nattr=train_set.attr_num)
model = FeatClassifier(backbone, classifier)
checkpoint = torch.load(
'/home/sohaibrabbani/PycharmProjects/Strong_Baseline_of_Pedestrian_Attribute_Recognition/exp_result/custom/custom/img_model/ckpt_max.pth'
)
model.load_state_dict({
k.replace('module.', ''): v
for k, v in checkpoint['state_dicts'].items()
})
for param in model.backbone.parameters():
param.requires_grad = False
if torch.cuda.is_available():
model = torch.nn.DataParallel(model).cuda()
criterion = CEL_Sigmoid(sample_weight)
param_groups = [{
'params':
filter(lambda p: p.requires_grad, model.module.finetune_params()),
'lr':
args.lr_ft
}, {
'params':
filter(lambda p: p.requires_grad, model.module.fresh_params()),
'lr':
args.lr_new
}]
optimizer = torch.optim.SGD(param_groups,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=False)
lr_scheduler = ReduceLROnPlateau(optimizer, factor=0.1, patience=4)
best_metric, epoch = trainer(epoch=args.train_epoch,
model=model,
train_loader=train_loader,
valid_loader=valid_loader,
criterion=criterion,
optimizer=optimizer,
lr_scheduler=lr_scheduler,
path=save_model_path)
print(f'{visenv_name}, best_metrc : {best_metric} in epoch{epoch}')
